1 Introduction

1.1 MERA Feedback

MERA continues to benefit from the feedback of many users. If you find a bug or have a suggestion please contact

1.2 Overview

Fisheries managers are in need of tools to inform decision making often in the face of imperfect information and high uncertainty.

By linking a simple user interface and questionnaire (Figure 1.1) to an operating model and sophisticated simulation software, MERA provides managers with an accessible and powerful tool for selecting management procedures that can achieve their performance objectives, checking that a management procedure that is performing as expected and calculating stock status.

MERA is designed to account for uncertainty in the fishery system, prioritizing robust management options and identifying value in alternative data collection and research programs. By focusing on operating models, MERA can provide quantitative outputs that are relevant to fishery legal frameworks and eco-certification standards, for example probabilistic estimates of stock status relative to reference levels. MERA lessens the reliance on subjective, qualitative scoring systems, increasing transparency and accountability in decision making. Furthermore since the App is compatible with the R statistical software operating models, management procedures and diagnostics are all customizable allowing for bespoke state-of-the art closed-loop simulation including MSE.


Figure 1.1. The MERA user interface.

1.3 How to use MERA

MERA has two inputs: a mandatory quantitative questionnaire and optionally, a standardized format for fishery data (Figure 1.2). The questionnaire contains a total of 30 questions, 19 regarding the fishery dynamics, 7 questions about the management system and a further 4 about the types and quality of data that are available (see Section 2 for details on all questions).

MERA has three principal modes that reflect the ways in which MERA can be used to inform management:

  1. Management Planning – determining a suitable management mode

  2. Management Performance – evaluating current management mode

  3. Status Determination – calculating stock status.

The first two use the quantitative questionnaire and (optionally) data to construct an operating model. If compatible data are loaded, the operating model will be automatically conditioned on those data.

The status determination mode requires additional user data to estimate stock status.

Where operating model features are required that are not included in the quantitative questionnaire it may be exported into the R statistical environment, modified and imported back into the MERA software. The three R packages that come under the openMSE umbrella are DLMtool (Carruthers and Hordyk 2018) and MSEtool (Hordyk et al. 2021) and SAMtool (Huynh et al. 2021). These are compatible with MERA operating models and data files and include a wide range of tools for modifying these to include custom dynamics such as ontogenetic habitat shifts, fine-scale movement dynamics, time varying movement, temporal changes in growth and natural mortality rate, shifting fleet size selectivity and economic constraints.


Figure 1.2. MERA components and workflow.

1.4 A note on skins

MERA includes considerable flexibility on how results of the three use-cases are presented. This is to allow various user groups to obtain results that are pertinent to their application and setting.

For the purpose of this documentation results are presented for the Marine Stewardship Council skin.


1.5 Background to MERA

1.5.1 The need for informed management

Fisheries management systems typically involve an ongoing cycle of harvest, data collection, data processing, resource assessment, management recommendations and enforcement of management measures (Walters and Martell 2004). Fisheries managers are embedded in these systems and must make critical decisions, many of which are often poorly informed.

For example, managers must apportion budgets among various species and competing programs such as data-collection, scientific research, resource assessment and enforcement. Managers must select an appropriate time interval for renewing management advice and the correct level of model complexity for assessing a stock. They must also select among many ways to interpret stock assessment outputs in terms of management advice (i.e. a harvest control rule). Additionally managers may be expected to guide their science program in the direction of the most critical uncertainties in the system that are most in need of research.

Given that it generally involves the use of public money in the stewardship of public resources it is problematic that even in developed fishery management systems, decision making is often ad-hoc, lacking both transparency in the basis for decisions such as those listed above, and a coherent overall strategy (but see IWC, other exceptions in Australian / South African fisheries where management strategy evaluation has been more prevalent, Punt et al. 2016).

1.5.2 Options for informing strategic management

There are two options for evaluating competing modes of management in order to inform management strategy: experimentation and theoretical modelling. Exploited ecological systems are a notoriously challenging subject for theoretical systems modelling. Due to biological, behavioral and ecological complexities (Rouyer et al. 2008, Sugihara et al. 2011), shifting productivity (Vert Pre et al. 2013), changing environmental conditions, unobserved exploitation (Agnew et al. 2009), variable quality of data and many other factors besides, models may be an inadequate representation of the system, failing to capture critical system dynamics and providing unreliable predictions.

Empirical testing recognizes these difficulties and evaluates alternative management modes applied in practice to the real system. However, for practical reasons the experimental approach is not feasible for most fisheries. Experimental replication is not possible when the subject is a single fish stock existing in a unique ecological setting precluding rigorous and statistically valid empirical evaluation. Even if it were feasible, the statistical power to detect system changes over relevant time horizons may be expected to be low and experimentation expensive, accounting for the costs of additional data collection and lost fishery yields. For such reasons, proposed experimental approaches to fishery management such as adaptive management (Holling 1978, Walters 2002) have not seen widespread adoption despite their empirical advantages (Walters 2007, Westgate et al. 2013).

The alternative, theoretical testing, relies on the development of representative systems dynamics models (operating models) to evaluate the expected performance of candidate management modes. While operating models can be used to inform a wide range of management questions, previous applications in fisheries have generally focused on Management Strategy Evaluation (MSE) in which various ways of setting management advice using data (‘management procedures’) are comparatively evaluated (Butterworth and Punt 1999, Punt et al. 2016).

While there have been criticisms of operational modelling (Rochet and Rice 2009, in reference to MSE), the potential advantages of the approach have made it on ongoing priority for developed fishery management systems at various scales for example, US state fisheries (CDFW 2018), federal fisheries in the US (NOAA 2019) and Canada (Kronlund et al. 2013) and for high seas tuna stocks (Anon 2018). Prevailing obstructions to more widespread development and adoption of operating models include relatively high costs (compared with a one-off assessment of the population) and the availability of suitably qualified analysts and data to inform various scenarios for system dynamics.

1.5.3 Contemporary approaches for informing fisheries management

In the absence of rigorous processes to establish operating models, fisheries resource management has often relied on subjective, qualitative frameworks for guidance. A large number of such frameworks exist that aim to evaluate biological risks (Productivity Susceptibility Analysis, PSA: Hobday 2007), stock status (Rapfish; Pitcher 1999), select management options (FISHE: EDF 2019) and prioritize management issues (Fletcher 2005). Such systems have been used widely. For example, PSA has been used in the eco-certification of global fish stocks (Seafoodwatch 2016) and satisfied legal requirements for ecosystem-based fishery management (Hobday et al. 2011).

Since they simply formalize expert judgement into a scoring system that accepts subjective inputs, it is hard to objectively evaluate both the validity of their assumptions and the quality of advice that they provide. When such systems have been codified and subject to theoretical testing their performance was found to be poor (e.g. biological risk assessment using PSA, Hordyk and Carruthers 2018).

1.5.4 MERA objectives

The primary objective of the MERA software (Carruthers et al. 2019) was to provide an accessible tool for the construction of operating models enabling fishery managers to make informed strategic decisions at various levels including data collection, species prioritization, MP selection and enforcement.

A secondary objective was to support an accessible user interface with sophisticated models and statistical libraries to allow for state-of-the-art operational modelling and MSE when required. Thirdly, MERA was designed to be applicable to the widest range of fisheries possible, various in their scientific understanding, data availability, biological, ecological and exploitation characteristics.

Lastly, MERA had to be customizable and inform diverse user groups including regional fishery management organizations, international development agencies such as the UN Food and Agricultural Organization and seafood certification bodies such as the Marine Stewardship Council

3 Questionnaire

3.1 Fishery Questions

The Fishery panel is a set of questions about the characteristics of the fish population and its fishery. The operating model parameter mappings of each answer are provided in Appendix Table A.1

3.1.1 Fishery description

Describe the fishery you are modelling and identify yourself and the relevant management agency.

The ‘Fishery start’ and ‘End’ dates are important and will be used to simulate your historical fishery. These are the years where there has been substantive fishing (that could impact the stock). The ‘End’ year after which an MP has been in use. When loading fishery data they must include this range of year. If indicator data are to be uploaded for the Management Performance mode, these data can be in years after the End year.

The text input box provides a place for you to document important qualitative aspects of the fishery that provide necessary context for the questionnaire including

  1. The history and current status of the fishery, including fleets, sectors, vessel types and practices/gear by vessel type, landing ports, economics/markets, whether targeted/bycatch, other stocks caught in the fishery.

  2. The stock’s ecosystem functions, dependencies, and habitat types.

  3. Any relevant reference materials and supporting documents, such as stocks assessments, research, and other analyses.

Figure 3.1. The fields of the fishery description question.

3.1.2 Longevity

How long lived is the fish species? This is a critical input determining stock productivity. The parameter M is the instantaneous natural mortality rate. For a review of data-limited methods of estimating M see Kenchington (2014).

Figure 3.2. An example of MERA specified longevity expressed as ‘maximum age’ and the instantaneous natural mortality rate M.

3.1.3 Stock depletion

Depletion D, refers to current spawning stock biomass relative to unfished.

Since depletion is a data-rich quantity it may not be readily quantified and it may be necessary to specify a wide range of uncertainty for this input to identify MPs that are suitably robust.

In a data-limited situation, coarse information regarding depletion may be obtained from examining length compositions, historical versus current catch rates, or by use of so-called Robin-Hood approaches.

For further information see Carruthers et al. (2014) and Punt et al (2011)

Figure 3.3. An example of MERA specified stock depletion - current spawnign stock biomass relative to ‘unfished’ levels.

3.1.4 Resilience

How resilient to exploitation is the stock? This question controls recruitment compensation - the extent to which recruitment is reduced from unfished levels (R0) as the spawning stock becomes increasingly depleted below unfishe levels (SSB0). Here resilence is expressed in terms of steepness (h): the fraction of unfished recruitment at 1/5 unfished spawning biomass.

For a useful review of compensatory density dependence in fish populations see Rose et al. (2001).

Figure 3.4. An example of MERA specified recruitment compensation (resilience) expressed as steepness: the fraction of unfished recruitment expected to occur at 1/5 unfishned spawning stock levels.

3.1.5 Historical effort pattern

What temporal pattern best describes the trend in historical annual fishing effort (e.g. boat-days per year, number of trips per year)?

Here the user clicks on the Figure to shape a historical pattern in effort.

If more than one times series of historical effort is given, historical fishing will be simulated subject to all trends in equal frequency.

This question specifies the possible range of mean trends, you will have an opportunity to adjust the extent of inter-annual variability and changes in fishing efficiency (catchability) in the following questions.

Here is an introduction to fishing effort courtesy of the UN FAO.

Figure 3.5. An example of MERA specified historical trend in fishing effort.

3.1.6 Inter-annual variability in historical effort

The extent of inter-annual variability in historical exploitation rates around the mean trend(s) specified in Fishery question #5. Again, here is the introduction to effort and exploitation rate by the UN FAO..

Figure 3.6. An example of MERA specified historical trend in fishing effort rate subject to additional inter-annual variability.

3.1.7 Historical fishing efficiency changes

The annual percentage increase or decrease in historical fishing efficiency. In targeted fisheries gear efficiency may improve over time given techological improvements in the gear, changes in fishing behavior, fish distribution and information sharing among fishers, among other things. Conversely, non-target or bycatch species may be subject to declining fishing efficiency due to regulations or avoidance behaviors. The catchability (q) is the fraction of available fish caught per unit of effort. For example, a 2% per annum increase in fishing efficiency means that after 35 years twice as many fish will be caught for the same effort as today.

The introduction to fishing efficiency by the FAO provides a basic summary and Arrenguin-Sanchez provide a more comprehensive review of catchability.

Figure 3.7. An example of MERA specified historical fishing efficiency changes.

3.1.8 Future fishing efficiency changes

This is similar to the previous question but determines the future relationship between fishing mortality rate and effort. This is a principal driver determining the performance differential of management procedures that set catch (TAC) versus effort advice (TAE).

Figure 3.8. An example of MERA specified future fishing efficiency changes.

3.1.9 Length at maturity

Size a maturity relative to asymptotic length (LM).

Note 1: ‘maturity’ as used by this model (and most fish population dynamics models) is not really whether a fish has fully developed gonads, but rather the fraction of maximum spawning potential per weight. For example, some fishes mature early, but at small sizes they spawn infrequently and their recruits have poor survival (low spawning fraction).

Note 2: asymptotic length is not the maximum length observed but rather the mean expected size of fish at their maximum age under unfished conditions

An ICES workshop report provides an overview of maturity estimation

Figure 3.9. An example of MERA specified maturity at length, relative to asymptotic length.

3.1.10 Selectivity of small fish

Fishing gear selectivity relative to asymptotic length (S) (ascending limb selectivity). For example, if 50% of 40cm fish are caught and maximum length is 100cm, S = 0.4.

The UN FAO provides an introduction to gear selectivity and how it may be quantified. For a more involved discussion on selectivity see the IATTC CAPAM workshop report

Figure 3.10. An example of MERA specified selectivity at length (relative to asymptotic length) for the ascending (smaller fish) limb of the curve.

3.1.11 Selectivity of large fish

Fishing gear selectivity of the largest individuals (SL). For example, if only 20% of the longest fish are caught by the gear SL = 0.2. Again here is the FAO introductory document and the IATTC CAPAM workshop report.

Figure 3.11. An example of MERA specified selectivity at length (relative to asymptotic length) for the descending (larger fish) limb of the curve.

3.1.12 Discard rate

Discard rate: what fraction of fish that are caught are discarded (includes fish that are dead and alive)?

The US National Marine Fisheries Service have a general guide to Understanding Fish Bycatch Discard and Escapee Mortality and one of the authors of that guide, Michael Davis also has a useful article: Key principles for understanding fish bycatch discard mortality.

Figure 3.12. An example of MERA specified discarding rate (fraction of fish caught that are released).

3.1.13 Post-release mortality rate

Post-release mortality rate (PRM). What fraction of discarded fish die after release?

Again here is NOAA’s general guide to Understanding Fish Bycatch Discard and Escapee Mortality and one of the authors and the article fo Michael Davis: Key principles for understanding fish bycatch discard mortality.

Figure 3.13. An example of MERA specified post-release mortality rate (fraction of fish that are discarded that subsequently die due to capture).

3.1.14 Recruitment variability

Interannual variability in recruitment (the coefficient of variation in log-normal recruitment deviations, sigma R). Recruitment is expected to change among years in response to changing spawning biomass levels. On top of this is additional variability that may be driven by many factors including varying ocean conditions, amount of spawning habitat, food availability and predation. Sigma R controls the extent of this additional variability in annual recruitments. For example, a sigma R of 10% means that 95% of recruitments will fall in approximately 80-120% of the mean recruitment predicted from spawning biomass.

Edward Houde authored a Comprehensive Review of Recruitment and Sources of Variability and if that isn’t sufficient there is Chambers and Trippel (1997).

Figure 3.14. An example of MERA specified annual recruitment variability expressed as a log-normal standard deviation.

3.1.15 Size of existing spatial closures

The size of a existing spatial closure (e.g. Marine Protected Area, MPA). The size A, is the % of habitat that is protected (the same fraction closed is applied to the habitats of all life stages, for example spawning and rearing grounds.

The FAO provides a comprehensive review of Marine Protected Areas.

Figure 3.15. An example of MERA specified historical spatial closure.

3.1.16 Spatial mixing in/out of existing spatial closures

The degree of stock mixing in/out of existing spatial closure. The degree of the spatial mixing of the fish stock is represented as the probability (P) of a fish leaving the spatial closure (i.e. the marine protected area, MPA) between years

Figure 3.16. An example of MERA specified mixing among existing open/closed areas.

3.1.17 Size of existing spatial closures

The size of a hypothetical future spatial closure (e.g. Marine Protected Area, MPA). The size A, is the % of habitat that is protected (the same fraction closed is applied to the habitats of all life stages, for example spawning and rearing grounds. This question addresses a possible future MPA allowing for the testing of MPs that use this closed area.

Figure 3.17. An example of MERA specified hypothetical future spatial closure.

3.1.18 Spatial mixing in/out of existing spatial closures

The degree of stock mixing in/out of the future hypothetical spatial closure. The degree of the spatial mixing of the fish stock is represented as the probability (P) of a fish leaving the spatial closure (i.e. the marine protected area, MPA) between years

Figure 3.18. An example of MERA specified mixing among future hypothetical open/closed areas.

3.1.19 Initial stock depletion

Initial depletion of the stock relative to asymptotic unfished levels (D1: spawning stock biomass in year 1 relative to equilibrium unfished conditions).

Many fisheries undertake large fluctuations in productivity. In some of these cases, a fishery may have began at a time when the stock was naturally low. This question provides an opportunity to specify this initial depletion. The default however is that the stock was at asymptotic unfished levels in the first year of the fishery

Figure 3.19. An example of MERA specified initial stock depletion (spawning stock biomass relative to unfished levels at the start of the historical simulated time period)

3.2 Management Questions

The Management panel is a set of questions about what fishery management options are available and how well management advice is followed.These questions:

  • identify what management procedures are feasible given the types of management measures.
  • determine the relative success of various management procedures that provide different types of advice.

The operating model parameter mappings of each answer are provided in Appendix Table A.2.

3.2.1 Types of fishery management that are possible

Here you indicate which MPs are feasible given the management options that are available. Management procedures can provide management advice in terms of:

  • Total Allowable Catch limits (TACs, e.g. 20,000 metric tonnes).
  • Total Allowable Effort (TAE, e.g. 800 trap days per year).
  • Size limits (e.g. minimum size of 45cm).
  • Time-area closures (e.g. closing an area to fishing, an MPA or a Winter closure.

For more information see the UN FAO guide to fishery management types.

Or alternatively, Steffanson and Rosenberg describe and discuss fishery managment types in their 2005 paper.

3.2.2 TAC offset

The possible extent to which fishing operations may exceed (overages) or fall short (underages) of the specified Total Allowable Catch (TAC)? For example, given a TAC of 1000 tonnes a 10% offset (overage) would on average lead to 1100 tonnes of fish taken.,

The FAO provides a cursory introduction to uncertainties in fisheries management including implementation error here.

Fulton et al. provide a discussion of implementation error in their 2011 paper.

Figure 3.20. An example of MERA specified TAC offset.

3.2.3 TAC implementation variability

In the previous question you specified the range of the possible TAC offset (mean overage or underage).In this question you add the variability (V) in the implementation of TACs among years.

For example, if on average thereis no TAC offset, a V of 10% leads to annual overages/underages within 20% of the annual TAC recommendation (the black line in the figure opposite) for 95% of cases.

The colored lines show the minimum and maximum variability superimposed on the lowest (dashed line) and highest (solid line) levels of overages/underages specified in the previous question.

Figure 3.21. An example of MERA specified TAC implementation variability.

3.2.4 TAE offset

What is the possible extent to which fishing operations may exceed (overages) or fall short (underages) of the specified Total Allowable Effort (TAE)?

For example, given a TAE of 2000 boat-days of fishing a 10% overage would on average lead to 2200 boat days of effort.

Note: you have the option of selecting MATCH TAC IMPLEMENTATION and mimicking the TAC offset (e.g. assuming that if more than the TAC is taken due to lack of enforcement there would be a similar discrepancy between recommended and implemented TAE)

Figure 3.22. An example of MERA specified TAE offset.

3.2.5 TAE implementation variability

In the previous question you specified the range of possible TAE offset (mean overages/underages). In this question you add the variability (V) in the implementation of TAEs among years.

For example, if on average there is no TAE offset, a V of 20% leads to annual TAE overages/underages within 40% of the annual TAE recommendation (the black line in the figure opposite) for 95% of cases.

The colored lines show the minimum and maximum variability superimposed on the lowest (dashed line) and highest (solid line) levels of overages/underages specified in the previous question.

As with the offset, you have the option of matching the TAC implementation variability.

Figure 3.23. An example of MERA specified TAE implementation variability

3.2.6 Size limit offset

What is the possible extent to which fishing operations may exceed (catch larger) or fall short (catch smaller) fish than the specified minimum size limit? For example, given a size limit of 20cm (e.g. escape hole size of a trap), a value of 20% would lead to a mean minimum size in the catch of 24cm.

Note that if you match TAC implementation variability this will be inverted for size limits (going under the minimum size limit is assumed equivalent to going over the TAC).

Figure 3.24. An example of MERA specified size-limit offset

3.2.7 Size limit implementation variability

In the previous question you specified the range of possible mean violations of a minimum size limit. In this question you add variability (V) in size limit implementation among years.

For example, a size limit of 90cm is exceeded by an average of 10cm, a value of 5% leads to minimum catch sizes of between 90cm and 110cm (the black line in the figure opposite) for 95% of cases. The colored lines show the minimum and maximum variability superimposed on the lowest (dashed line) and highest (solid line) offset in size limit specified in the previous question.

Figure 3.25. An example of MERA specified size-limit implementation variability

3.3 Data Questions

The Data panel is a set of questions about what types of data are available and quality of the data that are available. These questions:

  • identify what management procedures are feasible given the types of data available.

  • determine the relative success of the various management types that rely on differing types of data.

3.3.1 Types of data that are available

Management procedures require various data. Where data types are unavailable some MPs may not be feasible.

Annual catches are yearly reporting landings (e.g. 135 tonnes in 1998, 159 tonnes in 1999, etc).

Relative abundance indices may be fishery-dependent such as catch-per-unit-effort data or fishery-independent such as an annual abundance survey.

In the context of annual catches and relative abundance indices, ‘historical’ refers to data going back to ‘unfished conditions’ (pre industrial fishing) such that catches may be used to reconstruct stock trajectory and indices may infer current stock depletion. In contrast, ‘recent’ refers to data available for least 5 years from today.

Effort data are annual observations of fishing effort such as boat days in 2002.

Growth data refers to parameter estimates for growth parameters such as von Bertalanffy growth parameter K and mean asymptotic length, L-infinity.

Size and age composition data are samples of size and ages in the catch going back at least 2 years from today.

3.3.2 Catch reporting bias

In some data-limited fisheries, incomplete monitoring of fishing operations may lead to under-reporting (and to a lesser extent over reporting) of annual catches.

For further discussion of catch under reporting see Agnew et al. (2009).

Figure 3.26. An example of MERA specified catch reporting bias

3.3.3 Hyperstability in indices

Is the primary index of relative abundance proportional to real biomass? Indices of relative abundance derived from fishery catch-per-unit effort may decline faster than real abundance (hyperdepletion) in cases where, for example, the species is being avoided or there has been attrition of high-density sub-population structure during early commericial fishing.

Conversely catch per unit effort data may respond slower than real biomass changes if the species is being targetted, there is range contraction of fishing toward high density areas as the stock declines or the population naturally forms aggregations. For example purse-seine fisheries are often strongly hyperstable since the fish per aggregation may remain high even at low stock sizes.

It may be generally assumed that a well designed fishery-independent survey is proportational to abundance but there are notable exceptions.

See Erisman et al. (1992) or Maunder et al. (2006).

Figure 3.27. An example of MERA specified non-linearity among indices and stock biomass

3.3.4 Overall data quality

What is the overall quality of data that are available?

  • Perfect Information: An unrealistic and idealized observation model for testing the theoretical performance of MPs.
  • Good quality: annual catches and abundance indices are observed with low error (<20% CV) and length/age composition data are numerous (~100 independent observations per year).
  • Data moderate: annual catches and abundance indices are observed with greater error (<30% CV) and length/age composition data are fewer (~40 independent samples per year).
  • Data poor: annual catches and abundance indices are imprecisely observed (<50% CV) and length/age composition data are sparse (~15 independent samples per year).

A description of the observation error model is included in Carruthers et al (2013) and a similar model was used by Carruthers et al. (2015).

4 Management Planning Mode

4.1 Background to management planning

Given what is currently known (and not known) about a stock, its fishery and research program:

  • what management approach is likely to best achieve management objectives (MP selection)?
  • what data collection processes are most important in determining management performance (Value of information)?
  • which current uncertainties have the greatest influence of management performance (Cost of current uncertainties)?

Management Planning is the most complex MERA mode and provides closed-loop simulation testing of numerous management procedures and diagnostics to help managers identify research priorities.

4.2 Running a closed-loop simulation

Closed-loop simulation iteratively projects the stock and fishery forwards in time, simulating data, generating management advice from MPs and then calculating the impact of this advice on the stock.

It follows that the user must select both a management interval (the duration before new management advice is calculated) and the MPs that should be tested.

The default settings for the management planning mode (Figure 4.1) are deliberately intended to be computationally less demanding for demonstration purposes. An Management interval of 8 years is selected which is relatively long for most fisheries and the ‘Demo’ MP set is a small subset of 5 MPs that run quickly (curE75 is 75% of current fishing effort, DCAC is depletion-corrected average catch (MacCall 2009), IT10 is an index target management procedure that allows for increases/decreases in TAC of 10%, matlenlim is a minimum size limit at the size at 50% maturity, MRreal is a marine reserved in area 1 with reallocation of fishing effort to the open areas).

Figure 4.1. The controls for the management planning mode.

Users can choose appropriate management interval and a larger range of MPs. The ‘All’ MP set includes the 80+ data-poor and data-moderate MPs included in the DLMtool package including a few simple data-rich assessments. The ‘Top 20’ MP set is a subset of these that includes 20 MPs that are generally among the best performing across a varied set of operating models. The user can select ‘No ref. MPs’ to exclude reference MPs (e.g. FMSY fishing and zero catches) that are included to frame MP performance. Alternatively the user can choose ‘Data-rich MPs’ to include in the analysis, 8 data rich state-space stock assessment based MPs from the MSEtool package. For operating models with more than 48 simulations, the user can select ‘Parallel comp.’ to distribute calculation over numerous processors (note that although it will generally run faster, this breaks the progress bar).

The management-planning mode runs two closed-loop simulations. The first (base) is for the depletion specified by the questionnaire (and optionally informed by operating model conditioning). The second set of simulations (rebuilding) quantifies hypothetical rebuilding performance and starts the forward projections at a user-specified depletion range. This is controlled by a slider ‘Start % BMSY from which to evaluate rebuilding’ and the default starting level is 50% BMSY levels.

4.3 Interpreting results of Management Planning

4.3.1 Biomass projection table for base operating model relative to limit reference point

The biomass trajectories are tabulated (Table 4.1), and phrased in terms of the probability (the fraction of simulations) that biomass is above half of BMSY in future years (half of BMSY is the limit reference point).

Table 4.1 shows an example projection for 20 MPs. In general projections of all MPs achieved relatively high probabilities of being over the limit reference level. The exceptions were delay-difference MPs providing effort advice (DDe and DDe75) that could drop to a 30-35% probability of being over the limit reference point.

Table 4.1. The probability that biomass is greater than half BMSY in future years for the base operating model. Probabilities of 50% or less are color-coded red, those above 90% are color coded green. MP type denotes the class of MP according to the type of advice it provides be it Total Allowable Catch (TAC), Total Allowable Effort (TAE), a Size Limit (SzLim) or spatial closure (MPA). The feasibility column indicates whether an MP can be applied in practice. When an MP is not available due to data-deficiencies the feasibilty column includes a ‘D’. When an MP cannot be applied due to restrictions on the type of advice management can provide (e.g. a size limit isn’t an option) then the feasibility column includes an ‘M’.

4.3.2 Biomass projection table for base operating model relative to target reference point

In addition to biomass relative to the limit reference point, biomass trajectories are tabulated (Table 4.2), and phrased in terms of the probability that biomass is above BMSY in future years (BMSY is the target reference point).

Table 4.2. The probability that biomass is greater than BMSY in future years for the base operating model.

4.3.3 Projection plot for base operating model

Accompanying Tables 4.1 and 4.2 is Figure 4.2 which shows a graphical representation of projected biomass for each MP. These plots include 90% (light blue) and 50% (dark blue) probability intervals, the median estimate (white line) and two example simulations (dark blue lines). The two horizontal grey lines represent BMSY and half of BMSY (target and limit reference points, respectively).

Figure 4.2. Biomass projection plots for eight management procedures given the base operating model.

4.3.4 Projection table for rebuilding operating model

Similarly to the base operating model, tables and figures are provided for the rebuilding scenario (the second closed-loop simulation starting from user-specified status) (Table 4.3), and phrased in terms of the probability (the fraction of simulations) that biomass is below half of BMSY in future years.

Again the DDe and DDe75 MPs provided poor biomass performance. The remaining MPs can be expected to rebuild the stock to some extent for most of the simulations.

Table 4.3. The probability that biomass is greater than half BMSY in future years for the rebuilding operating model.

4.3.5 Projection plot for rebuilding operating model

Accompanying Table 4.3 are a pair of projection plots (Figures 4.3 and 4.4, respectively) showing the long- and short-term biomass outcomes for each MP under the rebuilding scenario.

Figure 4.3. Long-term biomass projection plots for eight management procedures given the rebuilding operating model.

Figure 4.4. Short-term Biomass projection plots for eight management procedures given the rebuilding operating model.

4.3.6 Cost of current uncertainties

For each type of operating model uncertainty (that is specified in the answers of the MERA questionnaire), a range of values are sampled. After the closed-loop simulation is run, it is possible to evaluate how yield performance varied across the range in these answers, identifying those uncertainties that carry the highest yield differential (are potentially the most costly).

Cost of current uncertainties is illustrated in Figure 4.5. On the x-axis are the top-7 most contributory sources of uncertainty. The left-most questions are the most influencial with larger bars indicating the degree of impact on yields. The uncertainties on the x-axis are labelled according to their question number in the MERA questionnaire. For example, for the DBSRA MP (second from left panel, top row) the most important uncertainty was fishery question 10 (F10) selectivity. Across the range of sampled values for this parameter there was a different of 37% in long-term yield.

Figure 4.5. Cost of current uncertainties analysis. Each panel represents the closed-loop simulation testing of a single MP using the base operating model. The variability in long-term yield (expressed as a %) is evaluated across the uncertainty specified for each MERA question. Questions numbered F, M and D correspond to those in the Fishery, Management and Data panels.

4.3.7 Value of Information

Similarly to the cost of current uncertainties analysis, it is possible to evaluate the impact of observation processes on MP performance (Figure 4.6). The degree of observation error and bias was specified in the Data questions of the MERA questionnaire.

Perhaps not surprisingly, for the DBSRA MP (that takes stock depletion as an input) depletion bias was the most important process determining long-term fishery yields.

Figure 4.6. Each panel represents the closed-loop simulation testing of a single MP using the base operating model. The variability in long-term yield (expressed as a %) is evaluated across the uncertainty specified for various observation processes. Biases are long-term persistent over / under estimation of quantities. Errors are generally log-normal annual errors in quantities.

4.3.8 Yield projection

So far the Management Planning results have focused solely on biomass performance. However in most fishery management settings there is a well-established trade-off between yields and biomass outcomes. The yield projections (Figure 4.7) show future trajectories in fishery yield for each MP.

Figure 4.7. Yield projections phrased as a fraction of current yield for the base operating model.

4.3.9 Fishing mortality rate projection

In addition to biomass and yield projections, fishing mortality rate projections relative to FMSY are included in Figure 4.8to illustrate chronic over or under exploitation.

Figure 4.8. Fishing mortality rate projections relative to FMSY fishing, for the base operating model. .

4.3.10 Yield - Biomass trade-offs

Higher yields may be expected to be inversely related to biological status: a prevailing performance trade-off that fishery managers must navigate. The yield-biomass trade-off plots (Figure 4.9) provide a summary of the probability of achieving target and limit biomass levels whilst obtaining reasonably high yields over the long-term.

Figure 4.9 plots these probabilities for each MP, color coded according to the type of advice they provide. The top-right represents better performance, the bottom-left, worse performance.

In this example, output control (TAC) MPs are generally outperforming input controls. Among the best performing MPs are ‘DCAC’, ‘DD4010’, ‘DD’, and ‘HDAAC’. There is a relatively steep reduction in expected yield to obtain the better biomass outcomes obtained by the MP ‘MCD4010’.

Figure 4.9. Long-term biomass-yield performance trade-offs. The probability of exceeding the limit reference point (half BMSY, lefthand panel) and target reference points (BMSY, righthand panel) plotted against the probability of obtaining more than half reference yield (MSY). The legend refers to the class of MP according to the type of advice they provide. ‘Input’ are input controls such as size limits, effort controls or spatial closures. ‘Output’ MPs provide TAC advice. ‘Reference’ MPs are those that show theoretical performance of reference MPs as a yardstick for what is possible or desirable under idealised management situations.

5 Management Performance Mode

5.1 Background to Management Performance and Auxilliary Indicators

In situations where an MP has been adopted and used for management, it is possible to develop indicators that can detect whether the real fishery conditions have departed substantially from those that were simulated (and used to select the MP).

Often referred to as ‘Exceptional Circumstances’ protocols, these compare the posterior predicted data from the operating model with those that have been observed since MP adoption.

In most MSE settings, quite simple exceptional circumstances protocols are established that compare the posterior predicted probability intervals of operating model indices of abundance with those indices as they are gathered.

MERA uses a somewhat more advanced multivariate approach in which multiple types of data can be observed and compared with operating model predictions (Carruthers and Hordyk 2018) under a more general umbrella term ‘Auxilliary Indicators’.

5.2 Running a management performance evaluation

In the options panel of the MERA questionnaire there is the option to load fishery data. These data can include time series of relative abundance indices, mean length and catch data.

This data file follows the standard Data format of DLMtool (Carruthers and Hordyk 2018) and includes a slot ‘LHYear’. LHyear is the last historical year of data prior to MP adoption. After this reference year, all observed data can be compared to simulated data using the multivariate auxiliary indicators included in MERA to detect departures in system dynamics.

If a suitably formatted data file is uploaded to MERA, the option to calculate auxilliary indicators becomes available.

Figure 5.1. The controls for the management performance mode.

5.3 Interpreting results of Management Performance

5.3.1 Posterior versus observed data

When an MP was tested using closed-loop simulation, for every simulation, in every year of the projection simulated data were generated. These multiple simulated data sets provides a basis for comparison with a single observed data from the real fishery system whilst an MP was in use.

Since an MP is codified, the only explanations for the observation of data that differ from those of the simulation is that either the operating model is misspecfied (either historically and/or in the future) or the operating model is correct and by chance atypical data have been collected.

Figure 5.2. Posterior predicted data (48 simulations) for catch, mean length and a relative abundance index for the base operating model over the first 5 years of a projection (shaded regions) compared with observed (points) data over the same time period during which the DCAC MP was in use.

6 Status Determination Mode

6.1 Background to status determination

A large number of proposed methods of estimating stock status have been suggested in the literature that encompass formal stock assessments all the way to approaches that claim to rely on only catch data. A serious problem with the literature is that the comparative performance of these approaches has not be evaluated. It can be argued that fishery system dynamics are sufficiently unique that few general rules might exist regarding which methods of status evaluation are the most reliable - that these are eccentric to the particularities of a specific fishery.

To address this MERA includes a range of status determination approaches and tests their expected performance given the simulated conditions described by the questionnaire. This simulation testing provides a context for interpreting real status determinations that can be calculated from real fishery data, uploaded in the Options panel of the MERA questionnaire.

6.2 Running a Status Determination

In the options panel of the MERA questionnaire there is the option to load fishery data. These data can include time series of relative abundance indices, mean length and catch data.

Once loaded status determination can be run - it automatically detects what data types are available and identifies those status estimation methods approaches that are compatible (the user can select the approach from the settings menu).

Each modelling approach for estimating status relies on a varying combination of data types that are coded according to the data used:

  • C: catch data (annual)
  • I: index of relative abundance (annual)
  • M: mean length of fish in the catch (annual)
  • L: length composition data (year by length class)
  • A: age composition data (year by age)

Approaches that use only catch data or length compositions assume a pattern in annual fishing mortality rate defined by the annual fishing effort of Fishery Question 5 and the catchability changes of Fishery Question 7.

Figure 5.3. The controls for the status determination mode.

6.3 Interpreting results of Status Determination

6.3.1 Simulation testing

Under development

6.3.2 Status estimates

Under development

7 Preparing Fishery Data for MERA

7.1 Introduction

In this context, fishery data includes any information that can be used for one of two purposes:

  1. to condition an operating model (OM) for Management Planning, Management Performance and Risk Assessment modes approaches using closed-loop simulation testing (e.g., Management Strategy Evaluation; MSE)

  2. to estimate the status of an exploited stock in the Status Determination mode

This document describes the standardized fishery data format for the MERA which is the same as that used in the DLMtool and MSEtool R packages.

Using a standardized format for fishery data has the advantage that a the data can easily be applied for these three purposes without any requiring any re-organizing of the fishery data.

The Data Input File is a standardized file format for fishery data that uses a standardized spreadsheet format: either a CSV (comma separated file; file extension .csv’) or a MS Excel file (file extension .xlsx or .xls). Support for Google Sheets may be added later.

Users enter all available fishery data into the data input file, which can then be imported into MERA.

7.2 Templates

Templates for the Data Input file can be downloaded from an online repository.

Note that the links to the Data Input CSV File will open in the web browser. Save these files to your machine (usually by right-clicking in the browser) will file extensions ‘.csv’.

If using the DLMtool or MSEtool R packages in a R session, the template files can be generated by typing the following commands in the R console:

library(DLMtool)
DataInit("Example") # create example populated Input and Documentation files 
# or create blank templates:
DataInit("MyDataFile") 
# replace "MyDataFile" with the name of the data files you wish to create

7.3 Populating the Fishery Data Files

This section of the guide describes how to enter data in the Data Input file.

Important Note 1: It is important to note that, in most cases, the data input file allows only one entry for each data type. For example, multiple catch-at-age data sets may be available (e.g., from a commercial fishery and from a fishery-independent survey). However, to be used in stock assessment or analysis, the two data sets must be combined in some way (or one data set ignored if it is not considered representative or reliable). Consequently, the data included in the Data Input file must represent the best available data.

This same principle applies to other data entered into the data input file. The only exception is for indices of abundance where multiple indices are allowed. This is explained in more detail in later sections of this guide.

Important Note 2: It is important that the text in the first column of the input file (column A) is not modified at all. These names are used to import the data file into MERA or the R packages.

Important Note 3: The data input file requires both character string (i.e., text) and numeric inputs. The data format for each entry is described below. It is important that no text is entered into the entries that require numeric inputs. For example, “Previous TAC” is a numeric input. A value of ‘1000’ (without the quotations) is acceptable, while an input of ‘about 1000’ is not.

Important Note 4: Do not use any thousands separators. For example 1 000 000 and 1,000,000 may introduce errors during the data import. Entries like 1000000 are preferable.

The fishery data are grouped into 7 categories:

  1. Metadata
  2. Biological parameters
  3. Selectivity parameters
  4. Time-series information
  5. Catch-at-age data
  6. Catch-at-length data
  7. Reference points and other metrics

The following sub-sections describe the data inputs for each of data category.

7.4 Metadata

The metadata section has 9 entries (see Figure @ref(fig:metadata) for example). All entries must go in the second column (column B if using a spreadsheet program such as MS Excel).


Figure 6.4. Example Metadata entries in the Data Input file

7.4.1 Name

Text entry. A unique name for this data file.

7.4.2 Common Name

Text entry. The common name of the species.

The example data file is for data from a fishery for cobia.

7.4.3 Species

Text entry. The scientific name of the species.

The example data file includes the species name for cobia.

7.4.4 Region

Text entry. The region of the fishery.

The example data file assumes the fishery is in the Western Atlantic.

7.4.5 Last Historical Year

Numeric entry. The calendar year of either:

  1. when the most recent time-series data was collected, or
  2. in cases where an MSE has already been conducted for this species and new data has been collected since, the last historical year when the MSE was run. For example, if an MSE was conducted for this fishery in 2016 and new data has been collected since then, the last historical year is 2016.

The last historical year was 2011 in the example (Figure @ref(fig:metadata)).

7.4.6 Previous TAC

Numeric entry. The most recent total allowable catch (TAC). Leave blank if no TAC exists.

There was no existing TAC for the example cobia fishery.

7.4.7 Units

Text entry. The units of the TAC and catch data, e.g., ‘thousand tonnes’. Leave blank if no TAC or catch data exists.

The catch data in the example fishery is in units of ‘1000 lbs’.

7.4.8 Previous TAE

Numeric entry. The most recent total allowable effort limit (TAE). Leave blank if no TAE exists.

There was no existing TAE for the example cobia fishery.

7.4.9 nareas

Numeric entry. The number of spatial areas used in management. Only used for management procedures that set spatial closures. Leave blank if no spatial management is used or proposed. Don’t enter 0 or 1.

The default value is 2 areas, which will be used if no value is entered here.

7.5 Biology

The next section contains mean and uncertainty values for the biological parameters of the species. Leave any entry blank if the parameter is unknown.


Figure 6.5. Example Biology entries in the Data Input file

7.5.1 Maximum age

Numeric entry. The maximum age of the species. The catch-at-age data entries must match this value (see the Catch-at-Age section for more details).

The cobia example has a maximum age of 16 (Figure @ref(fig:biology)).

7.5.2 M and CV M

Numeric entries. A point estimate for the (adult) natural mortality rate (M) and a coefficient of variation (CV) associated with this estimate (assuming a log-normal distribution).

The cobia example has an estimate of M of 0.26 and an associated CV of 0.3 (Figure @ref(fig:biology)).

7.5.3 von Bertalanffy Linf parameter and CV

Numeric entries. The estimated mean asymptotic length from a fitted von Bertalanffy growth model and the associated CV.

The units of the Linf parameter are not important, but all length parameters and data (e.g., length-at-maturity and catch-at-length) must be in the same units.

The cobia example has an estimate of Linf of 1324.4 and an associated CV of 0.23 (Figure @ref(fig:biology)).

7.5.4 von Bertalanffy K parameter and CV

Numeric entries. The estimated von Bertalanffy growth parameter (K) and the associated CV.

The K parameter must be in the same units as M, usually \(\text{year}^{-1}\).

The cobia example has an estimate of K of 0.27 and an associated CV of 0.07 (Figure @ref(fig:biology)).

7.5.5 von Bertalanffy t0 parameter and CV

Numeric entries. The estimated age when mean length is zero (t0) and the associated CV.

The t0 parameter must be in the same units as “Maximum age” (usually years).

The cobia example has an estimate of t0 of -0.47 and an associated CV of 0.05 (Figure @ref(fig:biology)).

7.5.6 Length-weight parameters

Numeric entries. Estimates of the a and b parameters (and associated CVs) from a fitted length-weight model of the form:

\[ W=aL^b\]

This data is not available for the example cobia data (Figure @ref(fig:biology)).

7.5.7 Recruitment parameters

Numeric entries. Mean estimates and associated CVs.

The steepness parameter is the expected fraction of virgin recruitment when the spawning biomass has been reduced to 20% of the unfished level. This is an important parameter for determining the productivity of the stock, especially at low levels of spawning biomass. However, the parameter is difficult to estimate and not well known for many species.

The sigmaR parameter describes the variance around the expected stock-recruitment relationship.

This data is not available for the example cobia data (Figure @ref(fig:biology)).

7.5.8 Length-at-Maturity parameters

Numeric entries. Mean estimates and associated CVs.

The Length at 50% maturity and Length at 95% maturity parameters are estimated by fitting a logistic model to maturity-at-length data. The parameters refer to the expected length where 50% and 95% respectively of the population are mature. The CV of length at 95% maturity is assumed to be the same as the CV of length at 50% maturity.

The example cobia data has estimates of the length at 50% and 95% maturity of 644 and 850 mm respectively, and a CV of 0.05.

7.5.9 Variability of length-at-age

Numeric entry. The expected variability of length-at-age; that is, the distribution of length-at-age around the mean growth curve described by the von Bertalanffy growth model.

The example cobia data assumed a coefficient of variability of length-at-age of 0.1.

7.6 Selectivity

There are five parameters relating to selectivity at length. Leave any entry blank if the parameter is unknown.


Figure 6.6. Example Selectivity entries in the Data Input file

7.6.1 Length at first capture

Numeric entries.

The Length at first capture is an estimate of first length class that is vulnerable to the fishery (and the associated CV).

The example cobia data file assumes a length of first capture of 130 and a CV of 0.2 (Figure @ref(fig:selectivity)).

7.6.2 Length at full selection

Numeric entries.

The Length at full selection is the first length class that is fully vulnerable to fishing (and the associated CV).

No information is available for the length at full selection for the example cobia data file (Figure @ref(fig:selectivity)).

7.6.3 Vulnerability at asymptotic length

Numeric entry.

The Vulnerability at asymptotic length describes that shape of the selectivity curve. Dome-shaped selectivity patterns (Vulnerability at asymptotic length < 1) occurs vulnerability to fishing begins to decrease after reaching a maximum value at some intermediate length.

The example cobia data file assumes asymptotic selectivity (Vulnerability at asymptotic length = 1) (Figure @ref(fig:selectivity)).

7.7 Time-Series

The time-series section includes data sources such as annual catches, annual abundance indices, and other annual indices such as recruitment, and mean length.


Figure 6.7. Example Time-series entries in the Data Input file


Figure 6.8. Example time-series entries in the Data Input file showing the entries continuing to the last historical year (2011)

Time-series data should be entered for all historical years of the fishery; that is, the first year the fishery began to the current year of data (Figures @ref(fig:ts1) and @ref(fig:ts2)). Years where no data are available should either be left empty or populated with an ‘NA’ (no quotations).

7.7.1 Year

Numeric entry. The calendar years the fishery has been operating. The years should begin in the first year of the fishery and sequentially increase to the last year where time-series data is available. This is usually the same as the Last Historical Year, unless more data has been collected since an MSE was conducted in the Last Historical Year.

In the cobia example, the fishery began in 1950 (Figure @ref(fig:ts1)) and time-series data exists until 2011 (Figure @ref(fig:ts2)).

7.7.2 Annual Catch and CV

Numeric entries.

Total catch records for each year (NA for missing years) in the same units as Units and Previous TAC.

CVs for each catch record should be included in the CV Catch row.

In the cobia example, we have catch records for every year, but no information on the CVs associated with these catches (Figures @ref(fig:ts1) and @ref(fig:ts2)).

7.7.3 Annual Effort and CV

Numeric entries.

Data on the annual total fishing effort and associated annual CVs. Effort can either be in absolute units (e.g., days at sea) or a relative trend in effort (e.g., ranging from 0 to 1). However, the effort data should be in the same units as Previous TAE (if available).

Leave blank or use NA for missing years.

There is no information on fishing effort for our cobia example (Figure @ref(fig:ts1)).

7.7.4 Abundance Index and CV

Numeric entries.

Relative or absolute annual index of total abundance and associated CVs. Leave blank if no data exists or use NA for years where data is missing.

The abundance index for the cobia example data set begins in 1981. All years before this are NA. There is no information on CV of the abundance index for the cobia example (Figures @ref(fig:ts1) and @ref(fig:ts2)).

7.7.5 Spawning Abundance Index and CV

Numeric entries.

Relative or absolute annual index of spawning abundance and associated CVs. Leave blank if no data exists or use NA for years where data is missing.

7.7.6 Vulernable Abundance Index and CV

Numeric entries.

Relative or absolute annual index of vulnerable abundance and associated CVs. Leave blank if no data exists or use NA for years where data is missing.

7.7.7 Additional Indices

Numeric entries.

The additional indices are optional, and are used in cases where multiple indices of abundance exist and are used either in combination or separately by management procedures.

There is no limit to the number of additional indices that can be added. The Data Input template has space for two additional indices (shaded in grey to indicate they are optional; Figure @ref(fig:ts1)).

Additional indices can be added by inserting 3 new rows for each additional index, with names in Column A following the form (ensure there are no spaces before or after the text):

  1. Index #
  2. CV Index #
  3. Vuln Index #

where # is a sequentially increasing integer for each additional index.

The data for the additional indices follows the same form as Abundance Index and CV. The only additional piece of information is the new row for the vulnerability schedule associated with the new index.

The vulnerability schedule for each index (e.g., Vuln Index 1) must be of length Maximum age and contain the vulnerability-at-age schedule associated with each index; i.e., values ranging from 0 to 1 indicating the vulnerability of each age-class that is represented in the additional index.

There are no additional indices for the example cobia dataset.

7.7.8 Recruitment Index and CV

Numeric entries.

Estimates of annual age-1 recruitment and associated CVs.

Leave blank if no data exists, or use NA to indicate years where data are missing. Must be the same length at Year.

There is no data on annual recruitment for the example cobia dataset. ### Mean Length Numeric entry.

Annual estimates of mean length of the vulnerable population (or catch).

Leave blank if no data exists, or use NA to indicate years where data are missing. Must be the same length at Year.

There is no data on annual mean length for the example cobia dataset.

7.7.10 Mean Length above Lc

Numeric entry.

Annual estimates of mean length above the modal length (Lc) of the vulnerable population (or catch).

Leave blank if no data exists, or use NA to indicate years where data are missing. Must be the same length at Year.

There is no data on annual mean length above Lc for the example cobia dataset.

7.8 Catch-at-Age

The catch-at-age section includes annual catch-at-age data.

If no catch-at-age data exists, leave this section blank.

Otherwise a row should be populated for each year that catch-at-age (in units of numbers) exists.


Figure 6.9. Example catch-at-age entries in the Data Input file

7.8.1 Vuln CAA

Numeric entry. Optional.

The default assumption is that the catch-at-age data is from the fishing fleet (i.e., the catch-at-age data reflects the aggregate vulnerability-at-age schedule from all fishing fleets).

If the catch-at-age data comes from a survey or a fleet with a different selectivity schedule, the vulnerability-at-age associated with the catch-at-age data should be entered here. The length of the Vuln CAA data should match the value entered in Maximum age.

Note that this data has not yet been implemented in the OM conditioning in the DLMtool and MSEtool R packages.

No alternative vulnerability schedule has been specified for the example cobia dataset (Figure @ref(fig:caa)).

7.8.2 CAA

Numeric entries.

A row should be entered for each calendar year of catch-at-age data that exists. Text entries in the first column for each new row should be of the from “CAA YEAR”, e.g. “CAA 1980”, “CAA 1981”, etc.

Catch-at-age (in numbers) should be entered for each age-class, from 1 to the maximum age specified in Maximum age.

The years in the catch-at-age data must be included in the years entered in the Year row. For example, “CAA 2019” is not valid if the years in Year only extend to 2015. In this case, increase the years in Year to 2019.

Years where catch-at-age data are missing can be left out, or entered with all values set to NA.

The cobia example has catch-at-age data from 1984 - 2011 (Figure @ref(fig:caa)).

7.9 Catch-at-Length

The catch-at-length section includes annual catch-at-length data.

If no catch-at-length data exists, leave this section blank.

Otherwise a row should be populated for each year that catch-at-length (in units of numbers) exists.


Figure 6.10. Example catch-at-length entries in the Data Input file showing the entries up to length class 710


Figure 6.11. Example catch-at-length entries in the Data Input file showing entries for length classes 860 to 1490

7.9.1 Vuln CAL

Numeric entry. Optional.

The default assumption is that the catch-at-length data is from the fishing fleet (i.e., the catch-at-length data reflects the aggregate vulnerability-at-length schedule from all fishing fleets).

If the catch-at-length data comes from a survey or a fleet with a different selectivity schedule, the vulnerability-at-length associated with the catch-at-length data should be entered here. The length of the Vuln CAL data should match the length of CAL_mids, or be one less than the length of CAL_bins.

Note that this data has not yet been implemented in the OM conditioning in the DLMtool and MSEtool R packages.

No alternative vulnerability schedule has been specified for the example cobia dataset (Figure @ref(fig:cal1)).

7.9.2 CAL_bins

Numeric entry. Optional, but either CAL_bins or CAL_mids must be populated if catch-at-length data is entered.

The vertices of the length classes for the catch-at-length data. Must increase sequentially.

The cobia example has length classes beginning at 170 mm and extending to 1490 mm (Figures @ref(fig:cal1) and @ref(fig:cal2)).

7.9.3 CAL_mids

Numeric entry.

Optional, but either CAL_bins or CAL_mids must be populated if catch-at-length data is entered.

The mid-points of the length classes for the catch-at-length data.

If both CAL_bins and CAL_mids are entered, they must correspond correctly; that is, CAL_mids is the mid-points of the vertices specified in CAL_bins, and length CAL_mids is one shorter than length CAL_bins.

As the CAL_bins data has been entered, the CAL_mids row has been left blank for the cobia example (Figure @ref(fig:cal1)).

7.9.4 CAL

Numeric entries.

A row should be entered for each calendar year of catch-at-length data that exists. Text entries in the first column for each new row should be of the from “CAL YEAR”, e.g. “CAL 1980”, “CAL 1981”, etc.

Catch-at-length (in numbers) should be entered for each length-class. That is, each row of CAL data should be the same length as CAL_mids or one less than the length of CAL_bins.

The years in the catch-at-length data must be included in the years entered in the Year row. For example, “CAL 2019” is not valid if the years in Year only extend to 2015. In this case, increase the years in Year to 2019.

Years where catch-at-length data are missing can be left out, or entered with all values set to NA.

The cobia example has catch-at-length data from 1984 - 2011 (Figures @ref(fig:cal1) and @ref(fig:cal2)).

7.10 Reference

The Reference section includes various biological reference points and other values that are by management procedures.

The example cobia dataset does not have any values for the Reference section, as no estimates of current depletion, abundance, or biological reference points exist for this fishery.


Figure 6.12. The reference section on the Data Input file

7.10.1 Current stock depletion and CV

Numeric entries.

An estimate of the current stock depletion (current spawning biomass divided by average unfished spawning biomass) and associated CV.

Usually obtained from a quantitative stock assessment.

Leave blank if no estimate is available.

7.10.2 Current stock abundance and CV

Numeric entries.

An estimate of the current absolute stock abundance (total biomass) and associated CV.

Usually obtained from a quantitative stock assessment.

Leave blank if no estimate is available.

7.10.3 Current spawning abundance and CV

Numeric entries.

An estimate of the current absolute spawning stock abundance (total biomass) and associated CV.

Usually obtained from a quantitative stock assessment.

Leave blank if no estimate is available.

7.10.4 Biological Reference Points and CV

Numeric entries.

The biological reference points: “FMSY/M” - the ratio of fishing mortality corresponding to maximum sustainable yield \(\left(F_\text{MSY}\right)\) to the natural mortality rate \((M)\) and “BMSY/B0” - the ratio of biomass corresponding to maximum sustainable yield \(\left(B_\text{MSY}\right)\) to the average unfished biomass \((B_0)\) are typically obtained from a quantitative stock assessment, or a yield-per-recruit analysis that accounts for the impact of reduced spawning biomass on the expected recruitment.

Leave blank if no estimates are available.

7.10.5 Catch Reference and CV

Numeric entries.

A catch level (in the same units as Catch), and associated CV, that is used by management procedures as a target level for catch.

7.10.6 Index Reference and CV

Numeric entries.

An index level (in the same units as Abundance Index, and associated CV, that is used by management procedures as a target level for the abundance index.

7.10.7 Duration t

Numeric entry.

In some cases, data are only used for a particular period of the fishery. For example, mean catches and depletion from the early years of a fishery when there was no management may be used.

The value in “Duration t” corresponds to the first t years of the history of the fishery that are used in the following two entries.

7.10.8 Average Catch over time t

Numeric entry.

The average catch over time t (and associated CV).

7.10.9 Depletion over time t

Numeric entry.

The estimated depletion at time t (and associated CV).

7.10.10 Reference OFL

Numeric entry.

A reference over-fishing limit (e.g., a catch limit). Leave blank if none exists.

7.10.11 Reference OFL type

Text entry.

A short description of the type of reference management elve (e.g., 2009 catch limit).

8 Importing the Fishery Data File

Once completed, the Data Input file can be imported into MERA or the R packages to produce management advice, estimate current stock status (if possible), or condition operating models for use in MSE.

8.1 Importing into MERA

The Data Input file can be uploaded into MERA.

More details will be added when MERA is released.

8.2 Importing into R Packages

The Data Input file can be imported into the MSEtool and DLMtool R packages with:

library(DLMtool) # or library(MSEtool)
Data <- XL2Data("MyDataFile.xlsx") # or Data <- XL2Data("MyDataFile.csv")

9 Support

9.1 MERA webpage

Under development

9.2 About DLMtool

For more information about DLMtool and MSEtool see Carruthers and Hordyk 2018

9.3 Other operating models

A range of operating models can be loaded into MERA from the DLMtool fishery library

9.4 More information on Management Procedures

You can view further documentation on any of the MPs featured in MERA by clicking on the following links:

10 Acknowledgements

10.1 Collaborators

MERA has benefitted greatly by the feedback and oversight of many people. Particular thanks to Katie Longo, Keith Sainsbury, Tony Smith, Sandy Morison, Kevin Stokes and Dave Newman for their careful feedback and guidance throughout software testing.

Thanks to Carlos Montero, Ricky Amoroso, Abdul ben Hasan, Roberto Licandeo and Brett van Poorten for their feedback during the testing phase.

10.2 MERA

MERA benefits from the ongoing support of the Packard Foundation, the Marine Stewardship Council, the Natural Resources Defense Council and the United Nations Food and Agricultural Organization.

10.3 DLMtool and MSEtool

Many thanks for the ongoing support of the Natural Resources Defense Council, and in particular DLMtool team members David Newman and Lisa Suatoni who have provided input at every stage. The development of DLMtool has been funded by the Gordon and Betty Moore Foundation, the Packard Foundation, Fisheries and Oceans Canada, the Walton Foundation, Resources Legacy Fund, the Natural Resources Defense Council, the United Nations Food and Agricultural Organization and the California Department of Fish and Wildlife.

10.4 Blue Matter Science

The openMSE framework and the MERA App were coded by Blue Matter Science Ltd (www.bluematterscience.com).

11 References

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Carruthers, T.R. & Hordyk, A.R. 2018. The Data-Limited Methods Toolkit (DLMtool): An R package for informing management of data-limited populations. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.13081

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Huynh, Q., Hordyk, A., Carruthers, T. 2021. SAMtool: stock assessment methods toolkit. R Package available on GitHub repository: www.github.com/blue-matter/SAMtool

Kronlund, A.R., Holt, K.R., Cleary, J.S., Shelton, P.A. 2013. Current approaches for the provision of scientific advice on the precautionary approach for Canadian fish stocks: Sections 8 – Management Strategy Evaluation. Canadian Science Advisory Secretariat. Research Document - 2013/081. Available at: http://www.dfo-mpo.gc.ca/csas-sccs/publications/resdocs-docrech/2013/2013_081-eng.html [accessed July 2019]

CDFW. 2018. Master plan for fisheries: a guide for implementation of the Marine Life Management Act. California Department of Fish & Wildlife. Available from: https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=159222&inline [accessed July 2019]

MERA. 2019. Version 1.0.1. Available at: www.MERAfish.org [accessed Jan 2021]

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Pitcher, T.J. 1999. Rapfish, A Rapid Appraisal Technique For Fisheries, And Its Application To The Code Of Conduct For Responsible Fisheries. FAO Fisheries Circular No. FIRM/C: No. 947: 47pp

Punt, A.E., Butterworth, D.S., de Moor, C.L., De Oliveira, J.A.A. & Haddon, M. 2016. Management strategy evaluation: best practices. Fish and Fisheries, 17, 303–334, http://dx.doi.org/10.1111/faf.12104.

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Rouyer, T., Fromentin, J.-M., Ménard, F., Cazelles, B., Briand, K., Pianet, R., et al. 2008. Complex interplays among population dynamics, environmental forcing, and exploitation in fisheries. Proc. Natl. Acad. Sci. U.S.A. 105, 5420–5425. doi: 10.1073/pnas.0709034105

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Sugihara, G., Beddington, J., Hsieh, C.-H., Deyle, E., Fogarty, M., Slaser, S. M., et al. 2011. Are exploited fish populations stable? Proc. Natl. Acad. Sci. U.S.A. 108, E1224–E1225. doi: 10.1073/pnas.1112033108

Thorson, J.T., Munch, S.B., Cope, J.M., Gao, J. 2017. Predicting life history parameters for all fishes worldwide. Ecological Applications. 27(8): 2262-2276. Doi: 10.1002/eap.1606.Walters, C. 2002. Adaptive management of renewable resources. Macmillan Publishing Co. New York. 347 pp. 

Vert-pre, K. A., Amoroso, R. O., Jensen, O. P., and Hilborn, R. 2013. The frequency and intensity of productivity regime shifts in marine fish stocks. Proc. Natl. Acad. Sci. U.S.A. 110, 1779–1784. doi: 10.1073/pnas.1214879110

Walters, C.J. 2007. Is adaptive management helping to solve fisheries problems? Ambio, 36, 304-307.

Walters, C.J., Martell, S.J.D. 2004. Fisheries Ecology and Management. Princeton University Press. 448 pp. 

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12 Appendices

12.1 Appendix A

This Appendix details the parameter mappings of the various answers of the MERA questionnaire.

Table App.1. The operating model parameter mapping of the answers for the various fishery questions.


Table App.2. The operating model parameter mapping of the answers for the various management questions.


Table App.3. The operating model parameter mapping of the answers for the various data questions.


  1. https://raw.githubusercontent.com/DLMtool/DLMtool/master/inst/Data.xlsx↩

  2. https://raw.githubusercontent.com/DLMtool/DLMtool/master/inst/Data.csv↩